Title : ( Improved mixed model for longitudinal data analysis using shrinkage method )
Authors: M. Rahmani , Mohammad Arashi , N. Mamode Khan , Y. Sunecher ,
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Abstract
The problem of multicollinearity among predictor variables is a frequent issue in longitudinal data analysis. In this context, this paper proposes a mixed ridge regression model via shrinkage methods to analyze such data. Furthermore, in view of obtaining more efficient estimators, we propose preliminary and Stein-type estimators using prior information for fixed-effects parameters. The model parameters are estimated via the EM algorithm. A simulation study is also presented to assess the performance of the estimators under different estimation methods. An application to the HIV data is also illustrated.
Keywords
, EM algorithm, Longitudinal data, Mixed model, Preliminary test, Stein estimation, Ridge regression
@article{paperid:1081523,
author = {M. Rahmani and Arashi, Mohammad and N. Mamode Khan and Y. Sunecher},
title = {Improved mixed model for longitudinal data analysis using shrinkage method},
journal = {Mathematical Sciences},
year = {2018},
volume = {12},
number = {4},
month = {December},
issn = {2008-1359},
pages = {305--312},
numpages = {7},
keywords = {EM algorithm; Longitudinal data; Mixed model; Preliminary test; Stein estimation; Ridge regression},
}
%0 Journal Article
%T Improved mixed model for longitudinal data analysis using shrinkage method
%A M. Rahmani
%A Arashi, Mohammad
%A N. Mamode Khan
%A Y. Sunecher
%J Mathematical Sciences
%@ 2008-1359
%D 2018
